arXiv:2508. 04928v5 Announce Type: replace-cross Abstract: We propose a method to extend foundational monocular depth estimators (FMDEs), trained on perspective images, to fisheye images.
By Rit Gangopadhyay, Jung-Hee Kim, Xien Chen, Patrick Rim, Hyoungseob Park, Alex Wong
arXiv:2608.23140v1 Announce Type: new
Abstract: Achieving 360{\deg} coverage is critical for the visual perception systems of autonomous vehicles. Fisheye cameras offer a cost-effective solution by e...
By Matthias Neuwirth-Trapp, Beg\"um Altunbas, Jiayi Wang, Yan Xia, Maarten Bieshaar, Xinyu Huang, Daniel Cremers
arXiv:2511. 20853v4 Announce Type: replace-cross Abstract: Training and evaluation of state-of-the-art computer vision algorithms for reliable shallow depth of field (DoF) rendering and defocus deblurring remain constrained by a persistent lack of large-scale, full-frame, high fidelity, real-image datasets.
By Nisarg K. Trivedi, Vinayaka A. Belludi, Li-Yun Wang
The paper introduces Distortion Extenders (DEX), learnable parameters that adapt vision foundation models to fisheye cameras by modeling distortion coefficients and correcting distributional shifts between fisheye and perspective images. DEX is applied to monocular depth estimation and open‑vocabulary segmentation across convolutional and Transformer architectures, consistently outperforming baselines on indoor and outdoor fisheye datasets. Additionally, DEX activations can be decoded to obtain distortion coefficients, aiding camera calibration.
By Rit Gangopadhyay, Alex Wong
The paper introduces a 2D Gaussian Splatting pipeline that renders a dominant-eye RGB image and depth proxy, then reprojects and selectively patches the affiliated eye to reduce redundant work. By reusing alpha-blending weights and generating adaptive regions of interest, the method cuts sequential binocular rendering time by 15.5% to 28.8% and GPU memory by 6% to 11% on several datasets, with minimal quality loss. It demonstrates a practical efficiency‑quality trade‑off for static‑scene stereo rendering and suggests further evaluation on dynamic scenes and VR hardware.
By Hongfei Zhu, Ling Zhou
FUSEye is a lightweight training framework that adapts a frozen COCO‑pretrained YOLO26‑x detector for fisheye images by adding only about 227k parameters. It introduces three modules: GridViews to enlarge compressed boundary regions, Z‑Adapters to correct distortion‑induced feature misalignment, and AgreeFusion to fuse detections across overlapping views. On the WoodScape fisheye benchmark, FUSEye boosts YOLO26‑x mAP50 from 0.148 to 0.266 while requiring only 25% of the labeled data to achieve 0.2597 mAP50, and it also improves YOLOv8‑11 detectors.
By Wenya Su, Kai Luo, Di Wen, Ruiping Liu, Yufan Chen, Junwei Zheng, Kunyu Peng, Kailun Yang